A Vector Quantized Autoencoder Model for the Interpretable Classification of Hepatic Ultrasound Textures

• Fabrizio P. Mello PUC Minas
• Alexei M. C. Machado PUC Minas / UFMG

DOI: https://doi.org/10.5753/sbcas.2026.21417

Resumo

Texture analysis in liver ultrasound is central to steatosis assessment, as pathological alterations are primarily reflected in microtextural patterns. This work proposes a vector quantized variational autoencoder (VQ-VAE) for faithful reconstruction and interpretable latent representation of hepatic textures. Experiments were conducted on 550 image patches of the liver, normalized by the hepatorenal index, where the reconstruction quality and discriminative capacity of the latent space were evaluated. The model based on VQ-VAE architecture achieved the best overall performance among the fully reconstructed autoencoder models with respect to Mean Squared Error, Structural Similarity Index and Peak Signal-to-Noise Ratio. Moreover, a Support Vector Machine (SVM) trained on 32-dimensional latent vectors achieved the same accuracy as a SVM trained on raw pixels while reducing dimensionality from 784 to 32 features. These results demonstrate that VQ-VAE preserves microtextures, organizes the latent space in a structured manner, and produces compact, discriminative representations, highlighting its potential for quantitative and interpretable liver ultrasound analysis.

Referências

Byra, M. et al. (2018). Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images. International Journal of Computer Assisted Radiology and Surgery, 13:1895–1903.

Chen, M., Shi, X., Zhang, Y., Wu, D., and Guizani, M. (2021). Deep feature learning for medical image analysis with convolutional autoencoder neural network. IEEE Transactions on Big Data, 7(4):750–758.

Constantinescu, E. C., Udris, toiu, A.-L., Udris, toiu, C., Iacob, A. V., Gruionu, L. G., Gruionu, G., Săndulescu, L., and Săftoiu, A. (2021). Transfer learning with pre-trained deep convolutional neural networks for the automatic assessment of liver steatosis in ultrasound images. Medical ultrasonography, 23(2):135–139.

Fetzer, D. T., Pierce, T. T., Robbin, M. L., Cloutier, G., Mufti, A., Hall, T. J., Chauhan, A., Kubale, R., and Tang, A. (2023). Us quantification of liver fat: past, present, and future. Radiographics, 43(7):e220178.

Gomide, L. C. and Machado, A. M. C. (2025). Classificação de texturas em imagens médicas através de modelos generativos e aprendizado autossupervisionado. In Anais do XXV Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS 2025), pages 967–972, Porto Alegre, RS, Brasil. SBC.

He, K., Chen, X., Xie, S., Li, Y., Dollár, P., and Girshick, R. (2022). Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 15979–15988.

Johnson, S. I., Fort, D., Shortt, K. J., Therapondos, G., Galliano, G. E., Nguyen, T., and Bluth, E. I. (2021). Ultrasound stratification of hepatic steatosis using hepatorenal index. Diagnostics, 11(8):1443.

Kingma, D. P. and Welling, M. (2014). Auto-encoding variational bayes. In International Conference on Learning Representations (ICLR).

Liang, Y. et al. (2024). Hierarchical vector-quantized variational autoencoder and vector credibility mechanism for high-quality image inpainting. Electronics, 13(10):1852.

Marshall, R. H., Eissa, M., Bluth, E. I., Gulotta, P. M., and Davis, N. K. (2012). Hepatorenal index as an accurate, simple, and effective tool in screening for steatosis. American journal of roentgenology, 199(5):997–1002.

Owjimehr, M., Danyali, H., and Helfroush, M. S. (2015). An improved method for liver diseases detection by ultrasound image analysis. Journal of Medical Signals & Sensors, 5(1):21–29.

Razavi, A., van den Oord, A., and Vinyals, O. (2019). Generating diverse high-fidelity images with vq-vae-2. In Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc.

Rezende, D. and Mohamed, S. (2015). Variational inference with normalizing flows. In Proceedings of the 32nd International Conference on Machine Learning (ICML), pages 1530–1538.

Stahlschmidt, F. L., Tafarel, J. R., Menini-Stahlschmidt, C. M., and Baena, C. P. (2021). Hepatorenal index for grading liver steatosis with concomitant fibrosis. PLoS One, 16(2):e0246837.

van den Oord, A., Vinyals, O., and Kavukcuoglu, K. (2017). Neural discrete representation learning. In Neural Information Processing Systems, pages 1–10, Long Beach.

Zsombor, Z., Rónaszéki, A. D., Csongrády, B., Stollmayer, R., Budai, B. K., Folhoffer, A., Kalina, I., Győri, G., Bérczi, V., Maurovich-Horvat, P., et al. (2023). Evaluation of artificial intelligence-calculated hepatorenal index for diagnosing mild and moderate hepatic steatosis in non-alcoholic fatty liver disease. Medicina, 59(3):469.